scholarly journals Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244029
Author(s):  
Mohamed Mansour ◽  
Saleh Alsulamy ◽  
Shaik Dawood
Keyword(s):  
Neural Network ◽  
Test Sample ◽  
Training Sample ◽  
International Standards ◽  
Iso 14001 ◽  
Economic Activities ◽  
Neural Network Approach ◽  
Industrial Organizations ◽  
Iso 14001 Certification ◽  
Environmental Performance Evaluation

The purpose of this study was to model the link between the implementation of ISO 14031 and ISO 14001. This study connects ISO 14031’s guidelines as independent variables to a dependent variable expressed by the ISO 14001 certification situation of industrial organizations based on the judgments of environmental managers in Saudi Arabia. Applying the quantitative approach using a survey with 596 responses from organizations functioning in 30 economic activities, a multi-layered neural network was trained to examine the relationship between standards and predict whether the organization is ISO 14001 certified in addition to testing the developed network on a group of collected cases. The results demonstrated the ability of the network to classify the organization’s certification status by 94.00% according to the training sample and its ability to predict 91.00% of the test sample, with an overall prediction efficiency of 91.30%. This work provides insights and adds to the environmental performance evaluation literature providing a neural network model based on ISO 14031 guidelines that can be extended to include other international standards such as ISO 9001. This study supports the merging of ISO 14001 with ISO 14031 into a binding standard.

scholarly journals EVALUATING ASSOCIATION BETWEEN IMPLEMENTATION OF ISO 14031 GUIDELINES AND ISO 14001 CERTIFICATION BY INDUSTRIAL SECTOR IN SAUDI ARABIA

2021 ◽  
Vol 29 (1) ◽  
pp. 61-71
Author(s):  
Mohamed Mansour ◽  
Saleh Alsulamy
Keyword(s):  
Saudi Arabia ◽  
Environmental Performance ◽  
Guideline Implementation ◽  
Research Problem ◽  
Industrial Sector ◽  
Environmental Indicators ◽  
Iso 14001 ◽  
Industrial Organizations ◽  
Iso 14001 Certification ◽  
Environmental Performance Evaluation

Conflicting research results regarding the application of environmental management systems on the environmental performance of industrial organizations between positive, negative, and no effect made studying this relationship a complex research problem. This study aimed to assess the extent of the commitment of industrial organizations in Saudi Arabia in applying ISO 14031:2013 guidelines to evaluate environmental performance and to study the association between the guideline implementation by ISO 14001:2015 certified and uncertified organizations. Using the descriptive approach, the association was identified between 13 independent variables representing ISO 14031:2013 guidelines for environmental performance evaluation and ISO 14001:2015 certification based on a 596 organizations survey conducted from May to December 2020, in addition to comparing the results of the study with similar studies. The results showed a medium positive correlation of ISO 14031 measurement variables with ISO 14001 certification. The study answered the question concerning the association of implementing of ISO 14031 guidelines to evaluate the environmental performance of ISO 14001 certified or uncertified organizations. Limited resources organizations should focus on monitoring environmental indicators and concentrate of planning activities to ensure the organizations uses environmental condition indicators data efficiently. Future studies are necessary to determine causal relationships, to develop specific environmental performance measures, and to integrate ISO 14031 in ISO 14001.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals THE NEURAL NETWORK MODEL OF INDIVIDUALS CREDIT RATING

2019 ◽  
Vol 8 (6) ◽  
Author(s):  
Ilyas I. Ismagilov ◽  
Linar A. Molotov ◽  
Alexey S. Katasev ◽  
Dina V. Kataseva
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Rating ◽  
Test Sample ◽  
Training Sample ◽  
Consumer Lending ◽  
The Neural Network ◽  
Intelligent Decision Support Systems ◽  
Model Training

This article solves the problem of constructing and evaluating a neural network model to determine the creditworthiness of individuals. It is noted that the most important part of the modern retail market is consumer lending. Therefore, an adequate and high-quality assessment of the creditworthiness of an individual is a key aspect of providing credit to a potential borrower. The theoretical and practical aspects of assessing the creditworthiness of individuals are considered. To solve this problem, the need for the use of intelligent modeling technologies based on neural networks is being updated. The construction of a neural network model required the receipt of initial data on borrowers. Using correlation analysis, 14 input parameters were selected that most significantly affect the output. The training and test data samples were generated to build and evaluate the adequacy of the neural network model. Training and testing of the neural network model was carried out on the basis of the analytical platform “Deductor”. Analysis of contingency tables to assess the accuracy of the neural network model in the training and test samples showed positive results. The error of the first kind on the data from the training sample was 0.45%, and the error of the second kind was 1.39%. Accordingly, the error of the first kind was not observed on the data from the test sample, and the error of the second kind was 2.68%. The results obtained indicate a high generalizing ability and adequacy of the constructed neural network, as well as the possibility of its effective practical use as part of intelligent decision support systems for granting loans to potential borrowers

Cars and the environment: A new approach to assessment through ISO 14001 certification of the car process

2003 ◽  
Vol 217 (1) ◽  
pp. 31-40 ◽  
Author(s):  
F Orecchini ◽  
D Sabatini
Keyword(s):  
Life Cycle ◽  
Production Process ◽  
Environmental Sustainability ◽  
Point Of View ◽  
International Standards ◽  
Iso 14001 ◽  
Pollutant Emission ◽  
Negative Effect ◽  
Set Up ◽  
Iso 14001 Certification

To change the negative effect of traffic increase and to diminish noxious pollutant emission and environmentally harmful consequences of the car's life cycle, innovative tools and radically changed approaches are needed. The present proposal of assessing the car and its environmentally related technological and functional factors as part of the entire mobility production process shows the applicability of ISO 14001 standards to the entire car process and could be a concrete aid to reaching the current European and international objectives and strongly encouraging environmentally friendly car design, manufacturing and lifestyles. The car influences the environment during every stage of its life cycle. This paper analyses and demonstrates the real possibility of certifying, through ISO 14001 standards, instead of an industrial site or organization, a car process. The car process is the part of the mobility production process implemented in car use. Environmental management systems (EMSs) have been developed to improve the environmental performance of organizations towards the diffusion of sustainability in industrial production. The ISO 14000 series of international standards are the most important reference for eco-management of any type of organization. The two target groups involved in the car process environmental certification procedures set are manufacturers/suppliers on the one hand, interested in environmentally compatible new markets and products, and clients/users on the other hand, interested in testing benefits and problems of the EMS set-up and general environmental sustainability behaviours. The car model analysed for the case study application is the Toyota Prius, from the environmental point of view one of the best on the market, precursor model of the next-generation electric hybrid vehicles.

scholarly journals Short term prediction of atmospheric temperature using neural networks

MAUSAM ◽  
2022 ◽  
Vol 53 (4) ◽  
pp. 471-480
Author(s):  
S. PAL ◽  
J. DAS ◽  
P. SENGUPTA ◽  
S. K. BANERJEE
Keyword(s):  
Neural Network ◽  
Minimum Temperature ◽  
Ground Level ◽  
Test Sample ◽  
Atmospheric Temperature ◽  
Training Sample ◽  
Test Cases ◽  
Backpropagation Method ◽  
Hidden Layer ◽  
Short Term Prediction

In this paper, a neural network based forecasting model for the maximum and the minimum temperature for the ground level is proposed. A backpropagation method of gradient-decent learning in multi-layer perceptron (MLP) type of neural network with only one hidden layer is considered. This network consists of 25 input nodes and two output nodes. The network is trained with a varying number of nodes in the hidden layer using a set of training sample and each of them is tested with a set of test sample. It accepts previous two consecutive days information (such as pressures, temperatures, relative humidities, etc.) as inputs for the estimation of the maximum and the minimum temperature as output. The network with 20 or less neurons in the hidden layer is found to be "optimum" and it produces an error within ±2° C for 80% of test cases.

scholarly journals Classification of brain tumors using the multilayer perceptron artificial neural network

2018 ◽  
Vol 16 (36) ◽  
pp. 190-198
Author(s):  
Raid Adnan Omar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Tumor ◽  
Multilayer Perceptron ◽  
Research Work ◽  
Test Sample ◽  
Training Sample ◽  
Statistical Characteristics ◽  
Correct Classification ◽  
Artificial Neural

Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect on how the network perform when predicting cases of brain tumor, contrast accounted for 64.3 %, correlation accounted for 56.7 %, and entropy accounted for 54.8 %. All remaining characteristics accounted for 21.3-46.8 % of normalized importance. The output of the neural networks showed that sensitivity and specificity were scored remarkably high level of probability as it approached % 96.

The Impact of ISO 14001 environmental standards on exports

2021 ◽  
Author(s):  
Juan S. Blyde
Keyword(s):  
Export Performance ◽  
International Standards ◽  
Iso 14001 ◽  
Information Frictions ◽  
Firm Level ◽  
Environmental Certification ◽  
Level Data ◽  
Iso 14001 Certification ◽  
The Impact

Analyses that examine the role of international standards on export performance has been concentrated on quality certifications. Very little is known about the impact of environmental certifications on exports. In this paper we employ firm-level data from Ecuador to assess the impact of the ISO 14001 environmental certification on export outcomes. The results show that holding an ISO 14001 increases the likelihood of becoming an exporter by 0.31 percentage points (equivalent to 4%), and that this positive effect is concentrated among large firms. We did not find evidence that the environmental certification has a causal impact on the level or the growth rate of exports. Consequently, the results suggest that the ISO 14001 certification is most useful in reducing information frictions, allowing firms to initiate export transactions.

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